In complexity principle especially, theorems normally join smoothness actions to circuit measurement steps (which can be another course of complexity steps which indicate lower Kolmogorov complexity but not vice versa).
We have now accomplished overtraining, which ought to let SGD to penetrate in the region. This doesn’t appear to make much big difference to the probabilities we get.
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We’re not expressing that discrete complexity steps totally seize what we care about for NNs! We do on the other hand Imagine that they're sufficiently applicable to be informative for The larger photograph, even though just as a proxy for what we basically care about.
Exclusively, I am wary of the main focus on measuring complexity for functions amongst finite sets, for instance binary features.
I do think I might have predicted that SGD vs. random look for might have a reasonably minimal impact on inductive biases. My pet concept for why neural nets have a bias toward simplicity is that there are a lot more ways for neural nets to encode basic features than sophisticated functions, i.e. larger areas of parameter Room for easy functions.
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 When you take out the metric, They are identical.  So you may have constrained power to forecast what the rate-distortion idea notion of complexity is going to say, when you finally put the metric again in.
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